geopandas

GeoPandas - Geospatial Data Analysis

GeoPandas enables you to perform spatial joins, geometric manipulations, and coordinate transformations using the familiar Pandas API. It treats "geometry" as just another column in a DataFrame, but one that knows how to calculate areas, distances, and intersections.

When to Use

• Reading and writing spatial formats (Shapefile, GeoJSON, GeoPackage, KML).
• Performing spatial joins (e.g., "which points fall inside this polygon?").
• Coordinating system transformations (reprojecting from Lat/Lon to Meters).
• Geometric analysis (calculating buffers, centroids, convex hulls).
• Thematic mapping (Choropleth maps).
• Calculating spatial relationships (contains, overlaps, touches, within).
• Working with OpenStreetMap data or satellite-derived vector data.

Reference Documentation

Official docs: https://geopandas.org/
Interactive tutorials: https://geopandas.org/en/stable/gallery/index.html
Search patterns: gpd.read_file, gdf.to_crs, gpd.sjoin, gdf.buffer, gdf.explore

Core Principles

The GeoDataFrame

A GeoDataFrame is a pandas.DataFrame that has at least one GeoSeries column (usually named geometry). Each row represents a feature (point, line, or polygon).

Coordinate Reference Systems (CRS)

Data without a CRS is just numbers on a grid. To perform real-world calculations (like area in km²), you must define the CRS (e.g., WGS84 - EPSG:4326 or UTM).

Predicates and Set Operations

Spatial analysis relies on binary predicates (intersects, within, contains) and set-theoretic operations (union, intersection, difference).

Quick Reference

Installation

pip install geopandas pyarrow pyproj fiona shapely

Standard Imports

import geopandas as gpd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from shapely.geometry import Point, LineString, Polygon

Basic Pattern - Load and Plot

import geopandas as gpd

# Load built-in dataset
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# Filter and Project
europe = world[world.continent == 'Europe']
europe = europe.to_crs(epsg=3035) # Equal Area projection for Europe

# Plot
europe.plot(column='pop_est', legend=True, cmap='viridis')